Geometric accuracy of low-dose CT scans for use in shoulder musculoskeletal research applications
Bibliographic record
Abstract
Computed tomography (CT) imaging is frequently employed in a variety of musculoskeletal research applications. Although research studies often use imaging protocols developed for clinical applications, lower dose protocols are likely possible when the goal is to reconstruct 3D bone models. Our purpose was to describe the dose-accuracy trade-off between incrementally lower-dose CT scans and the geometric reconstruction accuracy of the humerus, scapula, and clavicle. Six shoulder specimens were acquired and scanned using 5 helical CT protocols: 1) 120 kVp, 450 mA (full-dose); 2) 120 kVp, 120 mA; 3) 120 kVp, 100 mA; 4) 100 kVp, 100 mA; 5) 80 kVp, 80 mA. Scans were segmented and reconstructed into 3D surface meshes. Geometric error was assessed by comparing the surfaces of the low-dose meshes to the full-dose (gold standard) mesh and was described using mean absolute error, bias, precision, and worst-case error. All low-dose protocols resulted in a >70 % reduction in the effective dose. Lower dose scans resulted in higher geometric errors; however, error magnitudes were generally <0.5 mm. These data suggest that the effective dose associated with CT imaging can be substantially reduced without a significant loss of geometric reconstruction accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".